Introduction to Graph Data Science (GDS): Data-Driven RAG

Until now, we have retrieved information based on Patterns ("Find Sudeep's boss"). But what if we want to retrieve information based on Importance ("Find the most critical person in the company")? How do we calculate "Importance" without a human manually tagging every node? This is where Graph Data Science (GDS) comes in.

In this lesson, we will introduce the concept of "Algorithms on Topology." We will explore how GDS turns raw connections into numerical "Scores" for every node. We will look at the GDS Library (Neo4j) and understand how it uses the "Projected Graph" model to run heavy mathematical simulations without slowing down your production AI.

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1. What is Graph Data Science?

Graph Data Science is the application of mathematical algorithms to the structure of your graph.

• Standard Query: "Find the neighbor of A." (Search).
• GDS Algorithm: "If I start a random walk 1,000 times, which node do I land on most often?" (Probability/Importance).

Impact for RAG: GDS allows us to pre-calculate the "Relevance" of every fact in our graph. This means when an AI agent asks a question, we don't just give it the "Similar" facts—we give it the "Statistically Important" facts.

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2. The Projected Graph Model

Running complex math (like PageRank) on a 100-million node graph takes a lot of memory. To handle this, GDS systems use an In-Memory Projection.

1. Read: Select the part of the graph you want to analyze (e.g., just the PERSON and WORKS_AT relationship).
2. Project: Move a "Mathematical Copy" into a special high-speed RAM area.
3. Compute: Run the algorithm (e.g., Community Detection).
4. Write-Back: Save the results (scores) back to the original nodes as properties.

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3. The Big 3 Categories of GDS for RAG

1. Centrality: Who is the most important node? (PageRank, Degree, Betweenness).
2. Community: Who belongs together? (Leiden, Louvain).
3. Similarity: Which nodes are "Connected" in the same way? (Node2Vec, FastRP).

graph TD
    Raw[(Raw Graph)] -->|Project| RAM[In-Memory Mirror]
    RAM -->|Algorithm| Results[Numerical Scores]
    Results -->|Write Back| Raw
    
    subgraph "The GDS Loop"
    RAM
    Results
    end
    
    Raw -->|Query| AI[AI Agent]
    
    style RAM fill:#4285F4,color:#fff
    style AI fill:#34A853,color:#fff

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4. Implementation: Installing the GDS Plugin in Neo4j

If you are using Docker, you must enable the GDS library.

docker run \
    --name neo4j-gds \
    -p 7474:7474 -p 7687:7687 \
    -d \
    --env NEO4J_PLUGINS='["graph-data-science"]' \
    neo4j:latest

Once installed, you can call these algorithms directly from Cypher using the gds.* prefix.

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5. Summary and Exercises

GDS is the "Calculator" that adds a brain to your graph's memory.

• Projection allows for high-speed analysis without impacting query latency.
• Centrality measures the "Authority" of a piece of knowledge.
• Scalability: GDS allows you to derive insights from graphs that are too big for a human to read.
• Write-Back stores these "Mathematical Insights" as simple properties for the AI agent to use.

Exercises

1. GDS Choice: You want to find "The most influential document in our history." Do you use a standard Cypher query or a GDS algorithm?
2. Projection Drill: Why shouldn't you run a PageRank algorithm on your entire database including every single temporary node and relationship? (Hint: Memory limits and noise).
3. The "Math" Benefit: If Node A has a pagerank property of 0.9 and Node B has a pagerank of 0.1, which one should you send to the LLM if you only have space for 1 fact?

In the next lesson, we will look at the specific algorithms: Centrality Algorithms: Finding the Key Players.